Image fidelity is subjective, making it difficult to compare or assess changes or differences in images in a quantitative way. Quantitative metrics for assessing or comparing output images are important, because these metrics enable performance measurements of image output devices, such as computer printers, computer monitors, and televisions. These quantitative metrics also allow image fidelity of image output devices to be discussed in a measurable and non-subjective way.
Two existing methods for assessing image output are (1) qualitative assessments by a group of non-color deficient people and (2) measuring sample patches or spots of applied colors using a spectrophotometer, a calorimeter or, a densitometer and applying a formula, such as a CIE Delta E* formula, to compare the measurement to an expected value or another measured value. Qualitative assessment involves presenting output images to a group of non-color deficient people. The group reviews the output images and provides an assessment of the output images. Qualitative assessment by a group of people is useful in assessing image fidelity, since image fidelity is subjective, and the perception of people is what ultimately matters. However, since this method produces a variety of qualitative assessments (people typically have slightly different opinions), gauging changes or differences in output images with qualitative assessments by a group of people is difficult. Also, qualitative assessments are not particularly helpful in determining how much output images have improved or degraded, how much the output image needs to improve to be acceptable, or determine what an acceptable output image is.
Prior art FIG. 2 illustrates an example of measuring sample patches of applied colors to predict quality of an output image 101. In the example of FIG. 2, a color printer applies patches 100 or spots, each intended to have a uniform color. In the example, a cyan patch 102, a magenta patch 104, a yellow patch 106, a black patch 108, a white patch 110, and a grey patch 112 are defined. Each patch is measured using a spectrophotometer, a calorimeter, or a densitometer. A formula is applied to the measured value of each patch to arrive at a value for each patch. This value for each patch is compared for the correct value for the intended color of each patch. This method assumes that if the values for each patch are correct and the right amount of ink is put down, the quality of the image 101 should be acceptable. This method is used to control the quality of the print process, but does not directly measure the quality of the image 101.
Measuring color patches and applying a formula, such as CIE Delta E*, provides a quantitative metric to apply to image fidelity. However, measuring anything other than sizeable patches where the color is uniform is difficult. The color measurement in this method is measured for an area, averaging the color over that area. This method only addresses measuring how different, distinct colors appear, but doesn't address comparing the overall appearance of images. The results of this method could indicate that a color patch is good, when the appearance is very different than intended. For example, in the case of a yellow patch where cyan dots are added, the color measurement will average over the area, but a human eye will identify the contrast. In addition, measuring color patches does not address changes that do not affect color. For example, changes in resolution or dithering and halftoning that may result in Moirés patches are not addressed by color patch measuring.
There is a need for a method for measuring color image fidelity or quality that allows the overall appearance of an output image to be assessed in a qualitative way.